AI Application Engineer

TenX Semi; San Francisco (CA); Hybrid; USD 120,000 - 170,000; Full time

Job summary

TenX Semi in San Francisco Bay Area, CA is seeking an AI Application Engineer to transform unstructured inputs into structured representations used for chip design generation.

You will build parsers for PDFs and Word docs, analyze Verilog/SystemVerilog/C code, and create multi-modal understanding including diagrams and timing charts. You will implement Retrieval Augmented Generation pipelines and translate customer change requests into structured specs, collaborating closely with chip designers.

Qualifications

• Experience with large language models and AI agents.
• Expert-level Python and production ML systems.
• Experience extracting structured information from PDFs, Word docs, or other unstructured formats.
• Experience building retrieval augmented generation pipelines.
• Familiarity with AST parsing, static analysis, or compiler techniques.

Responsibilities

• Build parsers and extractors for PDFs, Word documents, wikis, and other unstructured formats—understand technical content, not just text.
• Develop pipelines to analyze Verilog, SystemVerilog, and C code to extract architectural intent, interfaces, and constraints.
• Create multi-modal understanding for diagrams, waveforms, timing charts, and tables—the visual language of chip design.
• Build retrieval augmented generation systems to provide context from large document corpora.
• Parse customer change requests into structured delta specifications for derivative design generation.
• Collaborate with chip designers to ensure captured architectural intent and constraints are accurate.

Skills

LLM Expertise

Python Proficiency

Document Parsing

RAG Systems

Code Analysis

Problem-Solving Mindset

Tools

AST Parsing

Static Analysis

Compiler Techniques

Embeddings

Vector Databases

Retrieval Systems

Job description

San Francisco Bay Area, CA · Hybrid · Full-time

We are looking for an AI Application Engineer who transforms unstructured materials—documents, code, specifications, diagrams—into structured representations that our AI system uses for chip design generation. You will build the bridge between messy real-world inputs and precise architectural specifications.

In this role, you will develop systems that can parse everything from PDF datasheets to legacy Verilog code, extracting the information our AI needs to generate and modify chip designs. Your work enables customers to use their existing documentation and collateral with our platform, dramatically reducing the barrier to AI-assisted chip design.

What You Will Do

• Build Document Understanding Systems: Develop parsers and extractors for PDFs, Word documents, wikis, and other unstructured formats. Your systems will understand technical content—not just extract text.
• Develop Code Analysis Pipelines: Build systems that analyze Verilog, SystemVerilog, and C code to extract architectural intent, interfaces, and constraints.
• Create Multi-Modal Understanding: Go beyond text. You will build systems that understand diagrams, waveforms, timing charts, and tables—the visual language of chip design.
• Build RAG Systems: Develop Retrieval Augmented Generation pipelines that provide relevant context from large document corpora to our design generation models.
• Extract Delta Specifications: Parse customer change requests and modification specs into structured formats that drive derivative design generation.
• Collaborate with Chip Designers: Work closely with chip design engineers to ensure your systems accurately capture architectural intent and design constraints.

What You Bring (Required)

• AI Agents and LLM Expertise: Deep experience with large language models and AI agents. Build applications on top of LLMs.
• Python Mastery: Expert-level Python skills and experience building production ML systems.
• Document Parsing Experience: Built systems that extract structured information from PDFs, Word documents, or other unstructured formats.
• RAG Systems: Experience building retrieval systems, working with embeddings, and integrating vector databases.
• Code Analysis: Familiarity with AST parsing, static analysis, or compiler techniques.
• Problem-Solving Mindset

Bonus Points (Preferred)

• Experience at AI labs or startup companies focused on AI.
• Experience with computer vision for diagram or waveform understanding.
• Knowledge of chip design concepts (architecture specs, interface protocols, timing diagrams).
• MS or PhD in Computer Science with NLP or ML focus.

Why Join Us

You will build AI that designs chips—one of the most complex engineering artifacts humans create. This isn't incremental improvement; it's a fundamental shift in how chips are designed.

• Shape the future of chip design by building "tools that build the chips"
• Work at the intersection of AI, automation, and silicon design
• Your software will multiply the productivity of entire engineering organizations
• Join a team that values software best practices applied to the hardware domain